PDL PROJECTS

I/O WORKLOAD CHARACTERIZATION

Data Mining meets Traffic Modeling

Traffic modeling of storage workloads is extremely helpful in evaluating
system designs. The work involves the following two aspects. The first
is to discover and to quantify the most important features of the traffic
data. Two example features are temporal burstiness and spatial locality.
In addition, it's even harder to determine how these features affect
the performance of the traffic data in real systems. Secondly, we need
an efficient statistical model to generate synthetic workloads of similar
behavior as the real ones. Traditional models such as Poisson are inadequate
in generating timestamps for traffic data of strong burstiness, not
mentioning generating multi-dimensional traffic.

This project is to solve the above problem. Our previous work has focused
on the spatio-temporal behavior of traffic data, more specifically,
the temporal burstiness and spatial locality of I/O workload. Our proposed
tool, entropy plot, is able to quantify the temporal burstiness and
spatial locality in traffic data. The B-model generates the timestamps
for the synthetic traffic to imitate the temporal burstiness of real
traffic data. The PQRS model goes one step further by generating both
the timestamps and request locations for synthetic traces. The ongoing
work is to augment the model to deal with more dimensionality.

2- and 3-dimensional representations of real traffic
data showing burstiness along time and space.